Correspondence Expansion for Wide Baseline Stereo

We present a new method for generating large numbers of accurate point correspondences between two wide baseline images. This is important for structure-from-motion algorithms, which rely on many correct matches to reduce error in the derived geometric structure. Given a small initial correspondence set we iteratively expand the set with nearby points exhibiting strong affine correlation, and then we constrain the set to an epipolar geometry using RANSAC. A key point to our algorithm is to allow a high error tolerance in the constraint, allowing the correspondence set to expand into many areas of an image before applying a lower error tolerance constraint. We show that this method successfully expands a small set of initial matches, and we demonstrate it on a variety of image pairs.

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Kevin L. Steele, Parris K. Egbert
• Keywords: Accurate Point Correspondences, Computer Vision, CVPR 2005, Error Tolerance Constraint, Small Initial Correspondence, Small Set, Strong Affine Correlation